code: def test_scheduler_outputs_equivalence(self):
      def set_nan_tensor_to_zero(t):
          t[t != t] = 0
          return t
  
      def recursive_check(tuple_object, dict_object):
          if isinstance(tuple_object, (List, Tuple)):
              for tuple_iterable_value, dict_iterable_value in zip(
                  tuple_object, dict_object.values()
              ):
                  recursive_check(
                      tuple_iterable_value, dict_iterable_value
                  )
          elif isinstance(tuple_object, Dict):
              for tuple_iterable_value, dict_iterable_value in zip(
                  tuple_object.values(), dict_object.values()
              ):
                  recursive_check(
                      tuple_iterable_value, dict_iterable_value
                  )
          elif tuple_object is None:
              return
          else:
              self.assertTrue(
                  torch.allclose(
                      set_nan_tensor_to_zero(tuple_object),
                      set_nan_tensor_to_zero(dict_object),
                      atol=1e-5,
                  ),
                  msg=(
                      "Tuple and dict output are not equal. Difference:"
                      f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
                      f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
                      f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
                  ),
              )
  
      kwargs = dict(self.forward_default_kwargs)
      num_inference_steps = kwargs.pop("num_inference_steps", 50)
  
      timestep = self.default_valid_timestep
  
      for scheduler_class in self.scheduler_classes:
          scheduler_config = self.get_scheduler_config()
          scheduler = scheduler_class(**scheduler_config)
  
          sample = self.dummy_sample
          residual = 0.1 * sample
  
          scheduler.set_timesteps(num_inference_steps)
          kwargs["generator"] = torch.manual_seed(0)
          outputs_dict = scheduler.step(
              residual, timestep, sample, **kwargs
          )
  
          scheduler.set_timesteps(num_inference_steps)
          kwargs["generator"] = torch.manual_seed(0)
          outputs_tuple = scheduler.step(
              residual, timestep, sample, return_dict=False, **kwargs
          )
  
          recursive_check(outputs_tuple, outputs_dict)
  
imports: import tempfile
  from typing import Dict, List, Tuple
  import torch
  from diffusers import LCMScheduler
  from diffusers.utils.testing_utils import torch_device
  from .test_schedulers import SchedulerCommonTest
label_desc: Use image node_id 14 for calling the LCMSchedulerTest obj's underlying member method code with example usage: obj.test_scheduler_outputs_equivalence() and returns: t